摘要
针对大学生就业信息管理系统存在功能不完善、响应速度慢等问题,设计了基于深度学习框架的大学生就业信息管理系统。系统硬件部分,通过采集模块获取学生的基本信息,在管理模块中对学生就业情况进行分析与管理,根据发布模块及时更新高校就业情况;系统软件部分,采用深度学习框架中的BP神经网络对大学生就业信息数据进行训练,降低信息录入误差率,提高系统信息管理能力。仿真实验结果表明,系统功能较为完善,响应时间较短。
In order to solve the problems of imperfect functions and slow response speed of the college student employment information management system,a college student employment information management system based on the deep learning framework is designed.The hardware part of the system obtains basic information of students through the acquisition module,analyzes and manages the employment situation of students under the management module,and updates the college employment in time according to the release module.The system software part uses the BP neural network in the deep learning framework to train the employment information data of college students,reduces the error rate of information entry,and improves the system information management ability.The simulation experiment results show that system function is relatively complete,and response time is faster.
作者
冯翊
FENG Yi(Xi’an Polytechnic University,Xi’an 710048,China)
出处
《信息技术》
2023年第8期107-111,共5页
Information Technology
关键词
深度学习
就业信息
信息管理
BP神经网络
反向传播
deep learning
employment information
information management
BP neural network
back propagation
